AI Product Development That Ships a Real v1
Production-grade AI products built on our own agentic pipeline: every commit reviewed, tested, and documented by the same agents we sell.
- Core workflow
- Auth + billing
- Agent + eval gate
- Cost caps + alerts
- Deploy + rollback
- Admin dashboards
- Multi-language
- Mobile app
- Custom SSO
- Marketplace
Why AI Products Die After the Demo
The demo works. Then a real user touches it. These are the four failure patterns that recur across AI products we get called in to rescue or rebuild.
Demo-ware in Production Clothing
Built on curated inputs and rehearsed flows. The first real user, malformed payload, or empty retrieval is what breaks it.
Quality Measured by Vibes
No eval harness, no golden set, no regression tracking. The acceptance test is that the demo did not crash.
The Junior Bench Problem
Seniors pitch the engagement and juniors write the code. The architecture reveals it around month six, when change gets expensive.
Scoped to Demo, Priced to Rebuild
Auth, tenancy, billing, and rollback skipped to hit the demo date. The rewrite costs more than the build did.
What We Build
Six kinds of AI build, each grounded in your data and measured before launch. Most products are one of these, or two of them wired together.
AI-Native SaaS Products
Full products with AI at the core: agent workflows, RAG, and copilots on a multi-tenant foundation built to scale.
Copilots & Assistants
Assistants that live inside your product, call your APIs for the facts, and write back answers your users can ship. Chatbot development →
Agent Systems
Autonomous agents that plan, call tools, and act under scoped credentials, eval gates, and a control plane.
AI Features, Existing Products
LLM features and automation added to the product you already run, without a rebuild of what works.
RAG & Document Intelligence
Retrieval over your documents and records with citations, confidence scores, and freshness from day one. RAG development →
Internal Tools & Automation
Internal copilots and workflow automation that clear the operational queue your team drowns in every week. Workflow automation →
Every Commit Runs Our Own Agentic Pipeline
The same pipeline that ships our three products runs on your build. This is what AI-accelerated delivery looks like when the tooling is yours.
What every commit clears
- PASSR reviews the PR across eight quality dimensionsEVERY PR
- TESTR generates tests from the diff, coverage trackedEVERY COMMIT
- DOCKR keeps docs and architecture diagrams in syncEVERY PUSH
- Eval gate scores every AI feature against the golden setEVERY RELEASE
- Cost telemetry attributed per feature, per callCONTINUOUS
- Human review on anything irreversibleALWAYS
What You Own at Launch
A working product and everything needed to run, extend, and defend it, in your accounts from day one.
The launch handover
- Production codebase in your repository, owned outright
- Architecture document with the decisions and the why
- Eval harness and golden test set for every AI feature
- CI/CD pipeline with rollback tested, not assumed
- Cost dashboard with per-feature token attribution
- Observability and alerting wired before launch
- Launch runbook and operations handover
- Post-launch observation window on live traffic
- v2 roadmap shaped by real usage data
Scoped by a Feasibility Study, Fixed at Kickoff*
The study draws the v1 cutline and prices the build. The build runs weekly-demo sprints to a launch gate. After launch, you choose what continues.
Feasibility Study
Validates the concept, draws the v1 cutline, and prices the build. Ends with a go or no-go verdict and a written cost model. Priced and scheduled separately.
The Build
Scope, timeline, and fee locked at kickoff. Week one is architecture; then weekly demos on real software running against real data until the launch gate.
Iterate or Hand Over
An observation window on live traffic is included. Then a retainer for iteration and new features, or a clean handover to your team with the full evidence trail.
*Build pricing depends on scope and is confirmed in the feasibility study. The number is locked before work starts.
MVP Mills, General Dev Shops, and Operator Engineers
AI product development splits between shops that build demos, shops that build everything, and teams that ship AI for a living. The difference shows up after launch.
Whatever demos well in week two.
Whatever the feature list says.
A v1 cutline agreed before the price is locked.
The demo did not crash.
Unit tests and QA sign-off.
An eval harness on your data, tracked across sprints.
A prompt wrapper around the demo path.
An API integration bolted on.
Agents, eval gates, and a control plane designed in week one.
Throwaway. Rebuilt after the raise.
Maintainable, if you pay for maintenance.
Reviewed, tested, and documented by agents on every commit.
Screenshots and a timeline promise.
Case studies and references.
Three live products shipped on the same pipeline.
| Dimension | MVP Mills | General Dev Shops | FLYTEBIT |
|---|---|---|---|
| What gets scoped | Whatever demos well in week two. | Whatever the feature list says. | A v1 cutline agreed before the price is locked. |
| How quality is proven | The demo did not crash. | Unit tests and QA sign-off. | An eval harness on your data, tracked across sprints. |
| The AI parts | A prompt wrapper around the demo path. | An API integration bolted on. | Agents, eval gates, and a control plane designed in week one. |
| The code | Throwaway. Rebuilt after the raise. | Maintainable, if you pay for maintenance. | Reviewed, tested, and documented by agents on every commit. |
| Proof | Screenshots and a timeline promise. | Case studies and references. | Three live products shipped on the same pipeline. |
Match the Tool to the Question
Product development answers how the product gets built and shipped. If that is not your question, one of these fits better.
Frequently Asked Questions
What does AI product development include?
End to end: scoping the v1, architecture, the build itself, an eval harness for every AI feature, deployment, and launch support. The deliverable is a working product in your repository, not a prototype or a slide deck.
How long does a v1 take?
A typical v1 runs 6 to 12 weeks depending on scope. Week one is architecture: data model, API surface, security, observability. Then weekly-demo sprints on real software until the launch gate. The exact window is set in the feasibility study, before the build price is locked.
How much does AI product development cost?
Cost depends on scope. The feasibility study, which starts from $2K, produces the v1 cutline and a costed estimate. The build price is then fixed at kickoff, so the number is agreed before work starts rather than discovered in change orders.
Who owns the code?
You do, from day one. The build lives in your repository under your accounts, with full IP assignment. There is no lock-in to our hosting, our tooling, or our team.
What makes your build process different?
Every build runs on our own agentic pipeline: PASSR reviews every pull request across eight quality dimensions, TESTR generates tests from the diff, and DOCKR keeps documentation in sync on every push. The same pipeline ships our three products, so the process is proven on software we operate ourselves.
Who do you build AI products for?
Founders and enterprises across industries. The work ranges from AI-native SaaS products and internal tools to AI features added to an existing product. What stays constant is the bar: production-grade from the first commit.
What happens after launch?
Every build includes a short observation window on live traffic. After that, you can keep us on a retainer for iteration and new features, or we hand over to your team with the runbook, eval harness, and architecture docs. Implementation oversight is also available if a different team takes over the build.
Ship a v1 That Survives Real Users
Schedule a 30-minute working session with our expert team. We will look at the product you want to build, the data it runs on, and give you a straight answer on what the v1 cutline should be.